Authors: Mingtao Wu, Heguang Zhou, Longwang Lucas Lin, Bruno Silva, Zhengyi Song, Jackie Cheung and Young Moon
Journal Title: MATEC Web of Conferences
ISSN: 2261-236X (Online)
Publisher: EDP Sciences
CyberManufacturing System is a vision for future manufacturing where physical components are fully integrated with computational processes in a connected environment. However, realizing the vision requires that its security be adequately ensured. This paper presents a vision-based system to detect intentional attacks on additive manufacturing processes, utilizing machine learning techniques. Particularly, additive manufacturing systems have unique vulnerabilities to malicious attacks, which can result in defective infills but without affecting the exterior. In order to detect such infill defects, the research uses simulated 3D printing process images as well as actual 3D printing process images to compare accuracies of machine learning algorithms in classifying, clustering and detecting anomalies on different types of infills. Three algorithms - (i) random forest, (ii) k nearest neighbor, and (iii) anomaly detection - have been adopted in the research and shown to be effective in detecting such defects.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Illustration Photo: Makerbot replicator 2 (credits: Scott Lewis / Flickr Creative Commons Attribution 2.0 Generic (CC BY 2.0))